Bot-Mod 提问式审核框架：从智能体社交网络中识别隐藏意图

View PDF HTML (experimental)

Abstract:The emergence of multi-agent systems introduces novel moderation challenges that extend beyond content filtering. Agents with $\textit{malicious intent}$ may contribute content that appears benign to evade content-based moderation, while serving a hidden objective. To address this challenge, we introduce Bot-Mod a moderation framework that grounds detection in agent intent rather than traditional content-level signals. Bot-Mod interrogates a target agent using a fixed set of diagnostic probes and infers its intent through Bayesian posterior inference over candidate intents. It starts with a calibrated content-only prior and updates the posterior using the $\textit{likelihood}$ of each answer under every candidate intent, as assigned by the moderator, until a stopping criterion is met. To evaluate our approach, we construct Molt-Intent, a dataset derived from Moltbook, capturing diverse benign and malicious behaviors within real community structures through posts and comments. Across Molt-Intent, an external social engineering dataset, and three agent LLMs, Bot-Mod outperforms content-only moderators, a fine-tuned classifier, and judge-based interrogators both in- and out-of-distribution. On Molt-Intent, it reaches a composite F1 of 0.736 in distribution and 0.684 out of distribution, 22% and 14% above the best baseline.

Subjects:	Artificial Intelligence (cs.AI); Social and Information Networks (cs.SI)
Cite as:	arXiv:2605.12856 [cs.AI]
	(or arXiv:2605.12856v3 [cs.AI] for this version)
	https://doi.org/10.48550/arXiv.2605.12856 arXiv-issued DOI via DataCite

Submission history

From: Ali Al Lawati [view email]
[v1] Wed, 13 May 2026 01:04:16 UTC (1,209 KB)
[v2] Thu, 14 May 2026 03:53:30 UTC (1,209 KB)
[v3] Mon, 26 Oct 2026 18:41:28 UTC (647 KB)